Introducing Context Intelligence
Many hopes and dreams are being fueled by the wonders and hype of AI systems. Yet those of us that have been through these technology-hype cycles before recognize the inevitable. AI technologies will help in…
PDR – 2024 Year in Review
Dear clients, partners, and friends, As we reach the end of 2024, it is once again a good opportunity to reflect on our achievements and learning over the past year, and to provide our perspective…
Egeria Advisor: Ingestion Tuning and the Multi-Agent Shift
In my last post, The Egeria Advisor: Casting Out Egeria Expert – Lessons Learned, I shared how we expanded the data scope of the Egeria Advisor to include core code repositories like egeria-python, egeria (Java), and egeria-workspaces. This expansion…
The Egeria Advisor: Casting Out Egeria Expert – Lessons Learned
In my last post, The Egeria Advisor: Sharing the Journey, I introduced the foundational vision behind the Egeria Advisor—injecting “Context Intelligence” into AI applications by leveraging organizational and situational context to provide more useful results. But…
The Egeria Advisor – Sharing the Journey
Dan Wolfson We have to build systems that people want to use, that provide value to them individually, that makes their work life better – not just view humans as knowledge sources to train the…
Responsibilities and activities supporting data readiness for AI projects
In my first post on Data Readiness, I introduced the notion of a Data Readiness activity to provide the AI development team with the political power to make the resources (typically data and subject matter…
Building the Data Lens for AI
A Data Lens is a specification of the data needed to support an AI development project. It reflects the scope of the business problem/opportunity laid out by the sponsors, but has sufficient detail to act…
Data and AI-led business transformation
An AI project often starts in a back room, as a series of experiments. Its operation at that time can be low key and informal. At the other end of the spectrum, an AI project…
Data Preparation in the AI Journey (Part 2)
In my previous post, I outlined five key pillars: Scope, Compliance, Trustworthiness, Understandability, and Cost. While these pillars provide a design framework, moving an AI application from an interesting experiment to a production-grade tool is not a…
Matching the AI Data Lens to the Data Sources
Scientific data includes context information (metadata) to make its creation reusable in downstream analysis. This metadata comes with a vocabulary that is helpful in understanding how business data – that is data from the systems…
Data Preparation in the AI Journey (Part 1) – From Strategy to Operations
It’s widely recognized that data preparation is critical for AI projects. But where does it actually fit in the development process? In my previous post, AI Application Development – Building with Context Intelligence, we explored…
AI Application Development – Building with Context Intelligence
From Planning to Building In our last installment, we discussed project definition and the context and clarity it can provide. In this installment, we’re going to jump into the part that most techies hanker for—the…